Recurrent Transformation of Prior Knowledge Based Model for Human Motion Recognition

Xu, Chunjing; He, Jie; Zhang, Xiaolin; Cai, Haipiao; Duan, Shihong; Tseng, Po-Hsuan; Li, Chong

doi:10.1155/2018/4160652

Cited by 7 publications

(4 citation statements)

References 17 publications

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“…The time series of human motion has always been an arrestive research topic [132]. Pedestrian detection is a significant portion of AD, and relevant researches have carried out in recent years [133], [134]. Reference [135]- [139] proposed corresponding vehicle motion models.…”

Section: A Motion Modelmentioning

confidence: 99%

Multi-Sensor Fusion in Automated Driving: A Survey

2020

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With the significant development of practicability in deep learning and the ultra-highspeed information transmission rate of 5G communication technology will overcome the barrier of data transmission on the Internet of Vehicles, automated driving is becoming a pivotal technology affecting the future industry. Sensors are the key to the perception of the outside world in the automated driving system and whose cooperation performance directly determines the safety of automated driving vehicles. In this survey, we mainly discuss the different strategies of multi-sensor fusion in automated driving in recent years. The performance of conventional sensors and the necessity of multi-sensor fusion are analyzed, including radar, LiDAR, camera, ultrasonic, GPS, IMU, and V2X. According to the differences in the latest studies, we divide the fusion strategies into four categories and point out some shortcomings. Sensor fusion is mainly applied for multi-target tracking and environment reconstruction. We discuss the method of establishing a motion model and data association in multi-target tracking. At the end of the paper, we analyzed the deficiencies in the current studies and put forward some suggestions for further improvement in the future. Through this investigation, we hope to analyze the current situation of multi-sensor fusion in the automated driving process and provide more efficient and reliable fusion strategies.

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Section: A Motion Modelmentioning

confidence: 99%

Multi-Sensor Fusion in Automated Driving: A Survey

2020

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“…c t −1 is cell output at the previous time stage, and c t is the state of memory at time t . Although LSTM performs promisingly and solves the vanishing/exploding gradient challenge of RNN [ 87 ] and overcomes backpropagation [ 90 ], it faces a complex structural challenge model. Also, memory cells that store time modes include multiple gateways to control information flow in and out of memory cells.…”

Section: Har Analysismentioning

confidence: 99%

Wearable Sensor-Based Human Activity Recognition in the Smart Healthcare System

Serpush

Menhaj

Masoumi

et al. 2022

Computational Intelligence and Neuroscience

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Human activity recognition (HAR) has been of interest in recent years due to the growing demands in many areas. Applications of HAR include healthcare systems to monitor activities of daily living (ADL) (primarily due to the rapidly growing population of the elderly), security environments for automatic recognition of abnormal activities to notify the relevant authorities, and improve human interaction with the computer. HAR research can be classified according to the data acquisition tools (sensors or cameras), methods (handcrafted methods or deep learning methods), and the complexity of the activity. In the healthcare system, HAR based on wearable sensors is a new technology that consists of three essential parts worth examining: the location of the wearable sensor, data preprocessing (feature calculation, extraction, and selection), and the recognition methods. This survey aims to examine all aspects of HAR based on wearable sensors, thus analyzing the applications, challenges, datasets, approaches, and components. It also provides coherent categorizations, purposeful comparisons, and systematic architecture. Then, this paper performs qualitative evaluations by criteria considered in this system on the approaches and makes available comprehensive reviews of the HAR system. Therefore, this survey is more extensive and coherent than recent surveys in this field.

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“…Combined with pooling and/or subsampling layers and fully connected special layers, CNNs are able to learn hierarchical data representations and classifiers that lead to extremely effective analysis systems. A multitude of applications are based on CNNs, including but not limited to [21][22][23][24].…”

Section: Related Workmentioning

confidence: 99%

Sequential Human Activity Recognition Based on Deep Convolutional Network and Extreme Learning Machine Using Wearable Sensors

Sun

et al. 2018

Journal of Sensors

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Human activity recognition (HAR) problems have traditionally been solved by using engineered features obtained by heuristic methods. These methods ignore the time information of the streaming sensor data and cannot achieve sequential human activity recognition. With the use of traditional statistical learning methods, results could easily plunge into the local minimum other than the global optimal and also face the problem of low efficiency. Therefore, we propose a hybrid deep framework based on convolution operations, LSTM recurrent units, and ELM classifier; the advantages are as follows: (1) does not require expert knowledge in extracting features; (2) models temporal dynamics of features; and (3) is more suitable to classify the extracted features and shortens the runtime. All of these unique advantages make it superior to other HAR algorithms. We evaluate our framework on OPPORTUNITY dataset which has been used in OPPORTUNITY challenge. Results show that our proposed method outperforms deep nonrecurrent networks by 6%, outperforming the previous reported best result by 8%. When compared with neural network using BP algorithm, testing time reduced by 38%.

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Recurrent Transformation of Prior Knowledge Based Model for Human Motion Recognition

Cited by 7 publications

References 17 publications

Multi-Sensor Fusion in Automated Driving: A Survey

Multi-Sensor Fusion in Automated Driving: A Survey

Wearable Sensor-Based Human Activity Recognition in the Smart Healthcare System

Sequential Human Activity Recognition Based on Deep Convolutional Network and Extreme Learning Machine Using Wearable Sensors

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